[1]     Xiao-Qian Liu, Xue-Ying Ding, Xin Luo, Xin-Shun Xu. Unsupervised Domain Adaptation via Class Aggregation for Text Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 2023. (Accepted)

[2]     Tian-Zi Niu, Shan-Shan Dong, Zhen-Duo Chen, Xin Luo, Shanqing Guo, Xin-Shun Xu. Semantic Enhanced Video Captioning with Multi-feature Fusion. ACM Transactions on Multimedia Computing Communications and Applications, 2023. (Accepted)

[3]     Tian-Zi Niu, Zhen-Duo Chen, Xin Luo, Peng-Fei Zhang, Zi Huang, and Xin-Shun Xu. Video Captioning by Learning from Global Sentence and Looking Ahead. ACM Transactions on Multimedia Computing Communications and Applications, 2023. (Accepted)

[4]     Xue-Ying Ding, Xiao-Qian Liu, Xin Luo, Xin-Shun Xu. DOC: Text Recognition via Dual Adaptation and Clustering. IEEE Transactions on Multimedia, 2023. (Accepted)

[5]     Tian-Zi Niu, Shan-Shan Dong, Zhen-Duo Chen, Xin luo, Zi Huang, Shanqing Guo, Xin-Shun Xu. A Multi-layer Memory Sharing Network for Video Captioning. Pattern Recognition, 2022. (Accepted)

[6]     Jie-Ru Tian, Yongxin Wang, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu. Diagnose Like Doctors: Weakly-Supervised Fine-Grained Classification of Breast Cancer. ACM Transactions on Intelligent Systems and Technology, 2022. (Accepted)

[7]     Yongxin Wang, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu. A High-Dimensional Sparse Hashing Framework for Cross-Modal Retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 2022.

[8]     Shan-Shan Dong, Tian-Zi Niu, Xin Luo, Wu Liu, Xin-Shun Xu. Semantic Embedding Guided Attention with Explicit Visual-feature Fusion for Video Captioning. ACM Transactions on Multimedia Computing Communications and Applications, 2022. (Accepted)

[9]     Peng-Fei Zhang, Zi Huang, Guangdong Bai, Xin-Shun Xu. IDEAL: High-Order-Ensemble Adaptation Network for Learning with Noisy Labels. ACM International Conference on Multimedia (MM), 2022.

[10]  Peng-Fei Zhang, Guangdong Bai, Zi Huang, Xin-Shun Xu. Machine Unlearning for Image Retrieval: A Generative Scrubbing Approach. ACM International Conference on Multimedia (MM), 2022.

[11]  Xinyu Liu, Xiaoqian Liu, Bin Wang, Xin Luo and Xin-Shun Xu. Arbitrary-shaped scene text detection with scoring mask quality.  IEEE International Conference on Multimedia & Expo (ICME), 2022.

[12]  Zhen-Duo Chen, Xin Luo, Yongxin Wang, Shanqing Guo and Xin-Shun Xu. Fine-grained hashing with double filtering. IEEE Transactions on Image Processing, 2022.

[13]  Peng-Fei Zhang, Yang Li, Zi Huang and Xin-Shun Xu. Aggregation-based graph convolutional hashing for unsupervised cross-modal retrieval. IEEE Transactions on Multimedia, 2022.

[14]  Yu-Wei Zhan, Yongxin Wang, Yu Sun, Xiao-Ming Wu, Xin Luo and Xin-Shun Xu.  Discrete online cross-modal hashing. Pattern Recognition, 2022.

[15]  Xiao-Ming Wu, Xin Luo, Yu-Wei Zhan, Chen-Lu Ding, Zhen-Duo Chen and Xin-Shun Xu. Online enhanced semantic hashing: Towards effective and efficient retrieval for streaming multi-modal data, AAAI Conference on Artificial Intelligence (AAAI), 2022.

[16]  Yongxin Wang, Jie-Ru Tian, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu. Label enhancement discrete cross-modal hashing method. Journal of Software, 2022. (Accepted, in Chinese)

[17]  Yan Wang, Yu-Wei Zhan, Xin Luo, Meng Liu,  and Xin-Shun Xu. Survey on video moment retrieval. Journal of Software, 2022. (Accepted, in Chinese)

[18]  Chen-Lu Ding, Xin Luo, Xiao-Ming Wu, Yu-Wei Zhan, Rui Li, Hui Zhang, and Xin-Shun Xu. Weakly-Supervised Online Hashing with Refined Pseudo Tags. ACM International Conference on Information and Knowledge Management (CIKM), 2022.  (Accepted)

[19]  Yongxin Wang, Zhen-Duo Chen, Xin Luo, Rui Li and Xin-Shun Xu. Fast cross-modal hashing with global and local similarity embedding. IEEE Transactions on Cybernetics, 2021.

[20]  Yu-Wei Zhan, Xin Luo, Yu Sun, Yongxin Wang, Zhen-Duo Chen and Xin-Shun Xu.  Weakly-supervised online hashing. IEEE International Conference on Multimedia & Expo (ICME), 2021.

[21]  Hong-Lei Yao, Yu-Wei Zhan, Zhen-Duo Chen, Xin Luo and Xin-Shun Xu. TEACH:  Attention-aware deep cross-modal hashing. ACM International Conference on Multimedia Retrieval (ICMR), 2021.

[22]  Hui-Qiong Li, Yongxin Wang, Zhen-Duo Chen, Xin Luo and Xin-Shun Xu. Ranking-based supervised discrete cross-modal hashing. Chinese Journal of Computers, 2021. (In Chinese)

[23]  Peng-Fei Zhang, Yadan Luo, Zi Huang, Xin-Shun Xu and Jingkuan Song. High-order nonlocal hashing for unsupervised cross-modal retrieval. World Wide Web, 2021.

[24]  Peng-Fei Zhang, Zi Huang and Xin-Shun Xu. Proactive privacy-preserving learning for retrieval. AAAI Conference on Artificial Intelligence (AAAI), 2021.

[25]  Yongxin Wang, Zhen-Duo Chen, Xin Luo and Xin-Shun Xu. High-dimensional sparse cross-modal hashing with fine-grained similarity embedding.  International World Wide Web Conference (WWW), 2021.

[26]  Yongxin Wang, Xin Luo, Liqiang Nie, Jingkuan Song, Wei Zhang and Xin-Shun Xu.  BATCH: A scalable asymmetric discrete cross-modal hashing. IEEE Transactions on Knowledge and Data Engineering, 2020. 

[27]  Xianjing Han, Xuemeng Song, Yiyang Yao, Xin-Shun Xu and Liqiang Nie. Neural compatibility modeling with probabilistic knowledge distillation.  IEEE Transactions on Image Processing, 2020.

[28]  Yongxin Wang, Xin Luo and Xin-Shun Xu. Label embedding online hashing for cross-modal retrieval. ACM International Conference on Multimedia (MM), 2020.

[29]  Yu-Wei Zhan, Xin Luo, Yongxin Wang and Xin-Shun Xu. Supervised hierarchical deep hashing for cross-modal retrieval. ACM International Conference on Multimedia (MM), 2020.

[30]  Zhen-Duo Chen, Chuan-Xiang Li, Xin Luo, Liqiang Nie, Wei Zhang and Xin-Shun Xu.  SCRATCH: A scalable discrete matrix factorization hashing framework for cross-modal retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 2020.

[31]  Zhen-Duo Chen, Yongxin Wang, Hui-Qiong Li, Xin Luo, Liqiang Nie and Xin-Shun Xu.  A two-step cross-modal hashing by exploiting label correlations and preserving similarity in both steps. ACM International Conference on Multimedia (MM), 2019.

[32]  Xuemeng Song, Xianjing Han, Yunkai Li, Jingyuan Chen, Xin-Shun Xu and Liqiang Nie. GP-BPR: Personalized compatibility modeling for clothing matching.  ACM International Conference on Multimedia (MM), 2019.

[33]  Yongqi Li, Meng Liu, Jianhua Yin, Chaoran Cui, Xin-Shun Xu and Liqiang Nie.  Routing micro-videos via a temporal graph-guided recommendation system.  ACM International Conference on Multimedia (MM), 2019.

[34]  Xue Dong, Xuemeng Song, Fuli Feng, Peiguang Jing, Xin-Shun Xu and Liqiang Nie. Personalized capsule wardrobe creation with garment and user  modeling. ACM International Conference on Multimedia (MM), 2019.

[35]  Jingkuan Song, Xiaosu Zhu, Lianli Gao, Xin-Shun Xu, Wu Liu and Heng Tao Shen.  Deep recurrent quantization for generating sequential binary codes.  International Joint Conference on Artificial Intelligence (IJCAI), 2019.

[36]  Chen Cui, Wenjie Wang, Xuemeng Song, Minlie Huang, Xin-Shun Xu and Liqiang Nie. User attention-guided multimodal dialog systems. International ACM SIGIR Conference of Research and Development in Information Retrieval (SIGIR), 2019.

[37]  Chuan-Xiang Li, Ting-Kun Yan, Xin Luo, Liqiang Nie and Xin-Shun Xu. Supervised robust discrete multimodal hashing for cross-media retrieval. IEEE Transactions on Multimedia, 2019.

[38]  Heyan Huang, Xiaochi Wei, Liqiang Nie, Xianling Mao and Xin-Shun Xu. From question to text: Question-oriented feature attention for answer selection. ACM Transactions on Information and System Security, 2019.

[39]  Xin Luo, Ye Wu, Wan-Jin Yu and Xin-Shun Xu. Class consistent hashing for fast web data searching. World Wide Web, 2019.

[40]  Yongxin Wang, Xin Luo, Huaxiang Zhang and Xin-Shun Xu. Sparse manifold embedded hashing for multimedia retrieval. ICDE Workshops, 2019.

[41]  Wan-Jin Yu, Zhen-Duo Chen, Xin Luo, Wu Liu and Xin-Shun Xu. DELTA: A deep dual-stream network for multi-label image classification. Pattern Recognition, 2019.

[42]  Xin Luo, Peng-Fei Zhang, Zi Huang, Liqiang Nie and Xin-Shun Xu. Discrete hashing with multiple supervision. IEEE Transactions on Image Processing, 2019.

[43]  Wei Zhang, Yimeng Li, Weizhi Lu, Xin-Shun Xu and Xiangyang Ji. Learning intra-video difference for person re-identification. IEEE Transactions on Circuits and Systems for Video Technology, 2019.

[44]  Chuan-Xiang Li, Zhen-Duo Chen, Peng-Fei Zhang, Xin Luo, Liqiang Nie, Wei Zhang and Xin-Shun Xu. SCRATCH: A scalable discrete matrix factorization hashing for cross-modal retrieval. ACM International Conference on Multimedia (MM), 2018.

[45]  Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Xin-Shun Xu and Mohan Kankanhalli.  Multi-modal preference modeling for product search. ACM International Conference on Multimedia (MM), 2018.

[46]  Xin Luo, Xiao-Ya Yin, Liqiang Nie, Xuemeng Song, Yongxin Wang and Xin-Shun Xu. SDMCH: Supervised discrete manifold-embedded cross-modal hashing.  International Joint Conference on Artificial Intelligence (IJCAI), 2018.

[47]  Xin Luo, Liqiang Nie, Xiangnan He, Ye Wu, Zhen-Duo Chen and Xin-Shun Xu.  Fast scalable supervised hashing. International ACM SIGIR Conference of Research and Development in Information Retrieval (SIGIR), 2018.

[48]  Wenjie Wang, Minlie Huang, Xin-Shun Xu, Fumin Shen and Liqiang Nie. Chat more:  Deepening and widening the chatting topic via a deep model.  International ACM SIGIR Conference of Research and Development in Information Retrieval (SIGIR), 2018.

[49]  Ye Wu, Xin Luo, Xin-Shun Xu, Shanqing Guo and Yuliang Shi. Dictionary learning based supervised discrete hashing for cross-media retrieval.  ACM International Conference on Multimedia Retrieval (ICMR), 2018.

[50]  Xin Luo, Peng-Fei Zhang, Ye Wu, Zhen-Duo Chen, Hua-Junjie Huang and Xin-Shun Xu. Asymmetric discrete cross-modal hashing. ACM International Conference on Multimedia Retrieval (ICMR), 2018.

[51]  Xin Luo, Ye Wu and Xin-Shun Xu. Scalable supervised discrete hashing for large-scale search. International World Wide Web Conference (WWW), 2018.

              Computational Intelligence

              Professional English

              Machine Learning